Configurations are disclosed for presenting virtual reality and augmented reality experiences to users. The system may comprise an image capturing device to capture one or more images, the one or more images corresponding to a field of the view of a user of a head-mounted augmented reality device, and a processor communicatively coupled to the image capturing device to extract a set of map points from the set of images, to identify a set of sparse points and a set of dense points from the extracted set of map points, and to perform a normalization on the set of map points.
Legal claims defining the scope of protection, as filed with the USPTO.
1. A system for generating a three-dimensional (3D) model of a face of a user, the system comprising: a head-mounted display (HMD) configured to present virtual content to a user; an inward-facing imaging system comprising at least one eye camera, wherein the inward-facing imaging system is configured to image at least a portion of the face of the user while the user is wearing the HMD; an inertial measurement unit (IMU) associated with the HMD and configured to detect movements of the HMD; and a hardware processor programmed to: detect a trigger to initiate imaging of a face of the user, wherein the trigger comprises a movement detected by the IMU involving putting the HMD onto a head of the user or taking the HMD off of the head of the user; activate, in response to detecting the trigger, the at least one eye camera to acquire images; detect a stopping condition for stopping the imaging based on data acquired from at least one of the IMU or the inward-facing imaging system; analyze the images acquired by the at least one eye camera with a stereo vision algorithm; and fuse the images to generate a face model of the user's face based at least partly on an output of the stereo vision algorithm.
2. The system of claim 1 , wherein to detect the trigger, the hardware processor is programmed to: determine an acceleration of the HMD; compare the acceleration of the HMD with a threshold acceleration; and detect the trigger in response to a comparison that the acceleration exceeds the threshold acceleration.
3. The system of claim 1 , wherein the stopping condition is detected when a distance between the HMD and the head of the user passes a threshold distance.
4. The system of claim 1 , wherein the stereo vision algorithm comprises at least one of: a block-matching algorithm, a semi-global matching algorithm, a semi-global block-matching algorithm, a disparity map, a depth map, or a neural network algorithm.
5. The system of claim 1 , wherein the at least one eye camera comprises a first eye camera and a second eye camera, and wherein the first eye camera and the second eye camera have an overlapping field of view.
6. The system of claim 5 , wherein the images comprises a plurality of pairs of images, wherein each pair of images comprises a first image acquired by the first eye camera and a second image acquired by the second eye camera.
7. The system of claim 6 , wherein a pair of images is analyzed together with the stereo vision algorithm.
8. The system of claim 6 , wherein the output of the stereo vision algorithm comprises depth assignments to pixels in the plurality of pairs of images.
9. The system of claim 6 , wherein the user's face is represented by a plurality of point clouds based on the analysis of the images acquired by the first eye camera and the second eye camera, and wherein to fuse the images to generate a face model, the hardware processor is programmed to: fit the plurality of clouds to one another; reject outliners in the plurality of clouds; and smooth a surface of the face model by at least one of clustering or averaging.
10. The system of claim 9 , wherein the fit the plurality of clouds, the hardware processor is programmed to apply Iterative Closest Point algorithm to the plurality of clouds.
11. The system of claim 1 , wherein the hardware processor is further programmed to: determine a texture map based on the images; and apply the texture map to the face model.
12. The system of claim 1 , wherein the hardware processor is further programmed to pass the face model to a wearable device.
13. The system of claim 1 , wherein to analyze the images, the hardware processor is programmed to at least: identify keypoints in the images using a keypoints detector and descriptor algorithm; or identify facial features from the images and describe the identified facial features with points in a 3D space.
14. The system of claim 13 , wherein to fuse the images, the hardware processor is programmed to combine the keypoints or facial features using a bundle adjustment algorithm.
15. A method for generating a three-dimensional (3D) model of a face of a user, the method comprising: receiving a request for generating a face model of a user; accessing images of the user's head acquired by an inward-facing imaging system of a wearable device, wherein the inward-facing imaging system comprises at least one eye camera; identifying a plurality of pairs of images from the accessed images; analyze the images by applying a stereo vision algorithm to the plurality of pairs of images; and fusing outputs obtained from said analyzing step to create a face model, wherein the images are acquired as the wearable is being put on or taken off from the user.
16. The method of claim 15 , wherein the outputs comprise a depth map associated with the user's face, which contains information relating to distances between the face and the wearable device.
17. The method of claim 15 , wherein the at least one eye camera comprises a first eye camera and a second eye camera, and a pair of images comprises a first image and a second image that are acquired at substantially the same time by the first eye camera and the second eye camera respectively.
18. The method of claim 15 , wherein analyzing the images comprise converting the plurality of pairs of images into point clouds.
19. The method of claim 18 , wherein fusing the outputs comprises combining the point clouds using an iterative closest point algorithm.
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November 26, 2018
December 1, 2020
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